Natural Language Processing (NLP) aims to convert human language into a formal representation that is easy for computers to manipulate. Current end applications include information extraction, machine translation, summarization, search and human-computer interfaces.
While complete semantic understanding is still a far-distant goal, researchers have taken a divide and conquer approach and identified several sub-tasks useful for application development and analysis. These range from the syntactic, such as part-of-speech labeling, chunking and parsing, to the semantic, such as word-sense disambiguation, semantic-role labeling, named entity extraction and anaphora resolution.
Currently, those tasks are typically analyzed separately. Many methods/systems possess few characteristics that would help develop a unified architecture which would presumably be necessary for deeper semantic tasks. In particular, many systems possess three failings in this regard: (i) they incorporate many hand-engineered features specific for one task; (ii) they cascade features learnt separately from other tasks thus propagating errors; and (iii) the systems are shallow in the sense that the classifier is often linear.
Accordingly, a method is needed which avoids the failings of prior methods.